Machine Learning for Wind Turbine Blades Maintenance Management

Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions a...

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Detalhes bibliográficos
Autores: Arcos Jiménez, Alfredo, Gómez Muñoz, Carlos Quiterio, García Márquez, Fausto Pedro
Tipo de documento: artigo
Data de publicação:2018
País:España
Recursos:Universidad Europea (UEM)
Repositório:ABACUS. Repositorio de Producción Científica
Idioma:inglês
OAI Identifier:oai:abacus.universidadeuropea.com:11268/6937
Acesso em linha:http://hdl.handle.net/11268/6937
Access Level:Acceso aberto
Palavra-chave:Turbinas eólicas
Aprendizaje automático
Turbina
Inteligencia artificial
Mantenimiento
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spelling Machine Learning for Wind Turbine Blades Maintenance ManagementArcos Jiménez, AlfredoGómez Muñoz, Carlos QuiterioGarcía Márquez, Fausto PedroTurbinas eólicasAprendizaje automáticoTurbinaInteligencia artificialMantenimientoDelamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using Machine Learning. Delaminations were induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule-Walker model is employed to features extraction, and Akaike’s information criterion method to the features selection. The classifiers are Quadratic Discriminant Analysis, k-Nearest Neighbours, Decision Trees and Neural Network Multilayer Perceptron. The Confusion Matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: Recall, Specificity, Precision and F-score.20182018-01-0220182018-01-0120182018-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/11268/6937reponame:ABACUS. Repositorio de Producción Científicainstname:Universidad Europea (UEM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:abacus.universidadeuropea.com:11268/69372026-06-11T12:41:27Z
dc.title.none.fl_str_mv Machine Learning for Wind Turbine Blades Maintenance Management
title Machine Learning for Wind Turbine Blades Maintenance Management
spellingShingle Machine Learning for Wind Turbine Blades Maintenance Management
Arcos Jiménez, Alfredo
Turbinas eólicas
Aprendizaje automático
Turbina
Inteligencia artificial
Mantenimiento
title_short Machine Learning for Wind Turbine Blades Maintenance Management
title_full Machine Learning for Wind Turbine Blades Maintenance Management
title_fullStr Machine Learning for Wind Turbine Blades Maintenance Management
title_full_unstemmed Machine Learning for Wind Turbine Blades Maintenance Management
title_sort Machine Learning for Wind Turbine Blades Maintenance Management
dc.creator.none.fl_str_mv Arcos Jiménez, Alfredo
Gómez Muñoz, Carlos Quiterio
García Márquez, Fausto Pedro
author Arcos Jiménez, Alfredo
author_facet Arcos Jiménez, Alfredo
Gómez Muñoz, Carlos Quiterio
García Márquez, Fausto Pedro
author_role author
author2 Gómez Muñoz, Carlos Quiterio
García Márquez, Fausto Pedro
author2_role author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Turbinas eólicas
Aprendizaje automático
Turbina
Inteligencia artificial
Mantenimiento
topic Turbinas eólicas
Aprendizaje automático
Turbina
Inteligencia artificial
Mantenimiento
description Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using Machine Learning. Delaminations were induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule-Walker model is employed to features extraction, and Akaike’s information criterion method to the features selection. The classifiers are Quadratic Discriminant Analysis, k-Nearest Neighbours, Decision Trees and Neural Network Multilayer Perceptron. The Confusion Matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: Recall, Specificity, Precision and F-score.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-02
2018
2018-01-01
2018
2018-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/11268/6937
url http://hdl.handle.net/11268/6937
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:ABACUS. Repositorio de Producción Científica
instname:Universidad Europea (UEM)
instname_str Universidad Europea (UEM)
reponame_str ABACUS. Repositorio de Producción Científica
collection ABACUS. Repositorio de Producción Científica
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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